Data-intelligent methods designed for forecasting the streamflow of the Fenhe River are crucial for enhancing water resource management. Herein, the gated recurrent unit (GRU) is coupled with the optimization algorithm improved grey wolf optimizer (IGWO) to design a hybrid model (IGWO-GRU) to carry out streamflow forecasting. Two types of predictive structure-based models (sequential IGWO-GRU and monthly IGWO-GRU) are compared with other models, such as the single least-squares support vector machine (LSSVM) and single extreme learning machine (ELM) models. These models incorporate the historical streamflow series as inputs of the model to forecast the future streamflow with data from January 1956 to December 2016 at the Shangjingyou station and from January 1958 to December 2016 at the Fenhe reservoir station. The IGWO-GRU model exhibited a strong ability for mapping in streamflow series when the parameters were carefully tuned. The monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The monthly IGWO-GRU model was found to be a better forecasting tool, with an average qualification rate of 91.66% in two stations. It also showed good performance in absolute error and peak flow forecasting.

Accurate prediction of runoff is of great significance for rational planning and management of regional water resources. However, runoff presents non-stationary characteristics that make it impossible for a single model to fully capture its intrinsic characteristics. Enhancing its precision poses a significant challenge within the area of water resources management research. Addressing this need, an ensemble deep learning model was hereby developed to forecast monthly runoff. Initially, time-varying filtered based empirical mode decomposition (TVFEMD) is utilized to decompose the original non-stationarity runoff data into intrinsic mode functions (IMFs), a series of relatively smooth components, to improve data stability. Subsequently, the complexity of each sub-component is evaluated using the permutation entropy (PE), and similar low-frequency components are clustered based on the entropy value to reduce the computational cost. Then, the temporal convolutional network (TCN) model is built for runoff prediction for each high-frequency IMFs and the reconstructed low-frequency IMF respectively. Finally, the prediction results of each sub-model are accumulated to obtain the final prediction results. In this study, the proposed model is employed to predict the monthly runoff datasets of the Fenhe River, and different comparative models are established. The results show that the Nash-Sutcliffe efficiency coefficient (NSE) value of this model is 0.99, and all the indicators are better than other models. Considering the robustness and effectiveness of the TVFEMD-PE-TCN model, the insights gained from this paper are highly relevant to the challenge of forecasting non-stationary runoff.

In order to explore long-term evolution rule and future trend of runoff time series, and exactly detect its tendency and long-range correlation characteristics, runoff data covering 1952–2012 from 3...